A surrogate based multi-fidelity approach for robust design optimization

Chakraborty, Souvik; Chatterjee, Tanmoy; Chowdhury, Rajib; Adhikari, Sondipon

doi:10.1016/j.apm.2017.03.040

Cited by 55 publications

(14 citation statements)

References 48 publications

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“…In this work, we are only interested in supervised learning techniques. Popular supervised learning techniques include Gaussian process or Kriging [19][20][21][22], Polynomial chaos expansion (PCE) [23][24][25], analysis-of-variance decomposition [26][27][28][29], Polynomial chaos based Kriging (PC-Kriging) [30][31][32][33] etc. In this work, we review three machine learning techniques in the context of stochastic low-velocity impact analysis.…”

Section: R E V I S E D P R O O Fmentioning

confidence: 99%

Stochastic Oblique Impact on Composite Laminates: A Concise Review and Characterization of the Essence of Hybrid Machine Learning Algorithms

Mukhopadhyay

Naskar

Chakraborty

et al. 2020

Arch Computat Methods Eng

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Due to the absence of adequate control at different stages of complex manufacturing process, material and geometric properties of composite structures are often uncertain. For a secure and safe design, tracking the impact of these uncertainties on the structural responses is of utmost significance. Composite materials, commonly adopted in various modern aerospace, marine, automobile and civil structures, are often susceptible to low-velocity impact caused by various external agents. Here, along with a critical review, we present machine learning based probabilistic and non-probabilistic (fuzzy) low-velocity impact analyses of composite laminates including a detailed deterministic characterization to systematically investigate the consequences of source-uncertainty. While probabilistic analysis can be performed only when complete statistical description about the input variables are available, the non-probabilistic analysis can be executed even in the presence of incomplete statistical input descriptions with sparse data. In this study, the stochastic effects of stacking sequence, twist angle, oblique impact, plate thickness, velocity of impactor and density of impactor are investigated on the crucial impact response parameters such as contact force, plate displacement, and impactor displacement. For efficient and accurate computation, a hybrid polynomial chaos based Kriging (PC-Kriging) approach is coupled with in-house finite element codes for uncertainty propagation in both the probabilistic and non-probabilistic analyses. The essence of this paper is a critical review on the hybrid machine learning algorithms followed by detailed numerical investigation in the probabilistic and non-probabilistic regimes to access the performance of such hybrid algorithms in comparison to individual algorithms from the viewpoint of accuracy and computational efficiency.

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Section: R E V I S E D P R O O Fmentioning

confidence: 99%

Stochastic Oblique Impact on Composite Laminates: A Concise Review and Characterization of the Essence of Hybrid Machine Learning Algorithms

Mukhopadhyay

Naskar

Chakraborty

et al. 2020

Arch Computat Methods Eng

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“…Further, more concrete problems are modelled: Ref. [53] addressed structure models for hydropower generation. Ref.…”

Section: Genealogy For Robust Design Research From Td Perspectivementioning

confidence: 99%

Research Series Review for Transdisciplinarity Assessment—Validation with Sustainable Consumption and Production Research

Sakao

2019

Sustainability

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In light of the escalating challenges for the sustainability of our societies, the need for improving the research of sustainable consumption and production (SCP) aiming to make real changes on the societies towards sustainability is evident. Transdisciplinary (TD) research is a promising way to enhance SCP research; however, insights to operationalize the concept of TD research are needed for both funders and researchers. Therefore, this article proposes an innovative way to capture and analyse a research series for transdisciplinarity assessment in qualitative and quantitative terms. This new way is termed research series review (RSR). This article adopted literature analysis and partly reflexive retrospective reasoning. In particular, citation content analysis was carried out in relation to two research series selected as the cases. The results show that RSR has advantages such as clearer traceability with cause-and-effect relationships. Furthermore, a successful SCP research series is hypothesised to form an iterative process between practical and theoretical fields as well as finding opportunities and proposing solutions.

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“…Therefore, it is necessary to consider the effect of uncertainty in an optimization process. In literature, there exist two methodologies to incorporate uncertainty into the framework of optimization, namely the robust design optimization (RDO) [1][2][3] and the reliability-based design optimization (RBDO) [4][5][6][7][8][9]. In this study, we focus on RBDO.…”

Section: Introductionmentioning

confidence: 99%

Threshold shift method for reliability-based design optimization

Goswami

Chakraborty

Chowdhury

et al. 2019

Struct Multidisc Optim

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We present a novel approach, referred to as the threshold shift method (TSM), for reliability based design optimization (RBDO). The proposed approach is similar in spirit with the sequential optimization and reliability analysis (SORA) method where the RBDO problem is decoupled into an optimization and a reliability analysis problem. However, unlike SORA that utilizes shift-vector to shift the design variables within a constraint (independently), in TSM we propose to shift the threshold of the constraints. We argue that modifying a constraint, either by shifting the design variables (SORA) or by shifting the threshold of the constraints (TSM), influences the other constraints of the system. Therefore, we propose to determine the thresholds for all the constraints by solving a single optimization problem. Additionally, the proposed TSM is equipped with an active-constraint determination scheme. To make the method scalable, a practical algorithm for TSM that utilizes two surrogate models is proposed. Unlike the conventional RBDO methods, the proposed approach has the ability to handle highly non-linear probabilistic constraints. The performance of the proposed approach is examined on six benchmark problems selected from the literature. The proposed approach yields excellent results outperforming other popular methods in literature. As for the computational efficiency, the proposed approach is found to be highly efficient, indicating it's future application to other real-life problems.Keywords RBDO · threshold shift method · PC-Kriging · uncertainty IntroductionUncertainty is an eternal companion of all structural systems. In a system, uncertainty can originate from both, uncertain parameters within the model as well as the limitations in the model itself. These uncertainties, if ignored, may result in to catastrophic failures. Therefore, it is necessary to consider the effect of uncertainty in an optimization process. In literature, there exist two methodologies to incorporate uncertainty into the framework of optimization, namely the robust design optimization (RDO) [1-3] and the reliability-based design optimization (RBDO) [4][5][6][7][8][9]. In this study, we focus on RBDO. * These authors have equal contributions

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A surrogate based multi-fidelity approach for robust design optimization

Cited by 55 publications

References 48 publications

Stochastic Oblique Impact on Composite Laminates: A Concise Review and Characterization of the Essence of Hybrid Machine Learning Algorithms

Stochastic Oblique Impact on Composite Laminates: A Concise Review and Characterization of the Essence of Hybrid Machine Learning Algorithms

Research Series Review for Transdisciplinarity Assessment—Validation with Sustainable Consumption and Production Research

Threshold shift method for reliability-based design optimization

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